A digital camera is a component often included in commercial electronic media device platforms. Digital cameras are now available in wearable form factors (e.g., video capture earpieces, video capture headsets, video capture eyeglasses, etc.), as well as embedded within smartphones, tablet computers, and notebook computers, etc. As illustrated in FIG. 1A, a camera 110 may acquire multiple images 101, 102 in a time-sequential manner (e.g., in video mode). As illustrated in FIG. 1B, multiple cameras 110, 190 embedded in the same device platform may acquire the images 101, 102 at one instant in time (e.g., in a stereo image mode). In either of the modes illustrated in FIGS. 1A and 1B, pixel values between images 101 and 102 may be mismatched over time and/or between cameras as a result of different exposures, white balance, and/or different camera sensor/pipeline response characteristics.
In many visual processing applications, such as object tracking, motion estimation, and disparity/depth estimation, the images 101 and 102 are processed to determine pixel correspondence between the two images. Pixel correspondence is often determined by computing errors/correlations between images, which further relies on the relative pixel value (e.g., color, intensity) ranges being normalized or matched during a pre-processing operation. Unfortunately, pixel value normalization can induce visual artifacts, particularly when performed on images with widely varying intensity levels. Such visual artifacts are also detrimental to subsequent pixel correspondence determinations.
Automated global matching of images that can be implemented by ultra light and low-power mobile platforms and results in fewer visual artifacts is therefore highly advantageous.